Multichannel Surface EMG Decomposition Based on Measurement Correlation and LMMSE. (28th June 2018)

• Record Type: Journal Article
• Authors: Ning, Yong; Zhao, Yuming; Juraboev, Akbarjon; Tan, Ping; Ding, Jin; He, Jinbao
• Other Names: Liu Chengyu Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2018(2018)
• Publication Date: 2018-06-28
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2018/2347589
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : A method based on measurement correlation (MC) and linear minimum mean square error (LMMSE) for multichannel surface electromyography (sEMG) signal decomposition was developed in this study. This MC-LMMSE method gradually and iteratively increases the correlation between an optimized vector and a reconstructed matrix that is correlated with the measurement matrix. The performance of the proposed MC-LMMSE method was evaluated with both simulated and experimental sEMG signals. Simulation results show that the MC-LMMSE method can successfully reconstruct up to 53 innervation pulse trains with a true positive rate greater than 95%. The performance of the MC-LMMSE method was also evaluated using experimental sEMG signals collected with a 64-channel electrode array from the first dorsal interosseous muscles of three subjects at different contraction levels. A maximum of 16 motor units were successfully extracted from these multichannel experimental sEMG signals. The performance of the MC-LMMSE method was further evaluated with multichannel experimental sEMG data by using the "two sources" method. The large population of common MUs extracted from the two independent subgroups of sEMG signals demonstrates the reliability of the MC-LMMSE method in multichannel sEMG decomposition.